The limitations of existing augmentation strategies for GCL inspire us to look back to the characteristics of the GNN encoder itself.
In Figure REF  we visualize the raw node features and the embedding of the randomly initialized untrained SGC [1]} activation layer.
It can be observed that without the help of trained model parameters, a more discriminative graph representation carrying semantics information can be obtained by neighbor information aggregation alone.
Thus, we argue that a better contrastive scheme should be tailored to the characteristics of GNNs (e.g., neighborhood aggregation).
